Abstract
Identifiability is a fundamental issue in measurement error models, because consistent estimation of all unknown parameters in the model is impossible if it is unidentifiable. In this Chapter we study this issue in linear and nonlinear regression models with either classical or Berkson type measurement error. We use a simple linear model to demonstrate how the measurement error in covariates causes its non-identifiability. We show that the nonlinear models with Berkson measurement error can be identified without prior restrictions on the parameters or extra data besides the main sample. We also show that the classical measurement error (errors in variables) models can be identified using the instrumental variable approach and provide a sufficient rank condition for the identifiability. Some examples are provided for illustration.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
